Over 50 years of research on social disparities in pain and pain treatment: a scoping review of reviews
Bibliographic record
Abstract
ABSTRACT: Research on social disparities in pain and pain treatment has grown substantially in recent decades, as reflected in a growing number of review articles on these topics. This scoping review of reviews provides a macrolevel overview of scholarship in this area by examining what specific topics and findings have been presented in published reviews. We searched CINAHL, Cochrane Database of Systematic Reviews, Embase, PsycINFO, PubMed, and Web of Science for English-language, peer-reviewed review articles, qualitative or quantitative, that aimed to characterize or explain pain-related differences or inequities across social groups. Of 4432 unique records screened, 397 articles, published over a 56-year period, were included. For each, we documented (1) axes of social difference studied (eg, sex/gender, race/ethnicity), (2) pain-related outcomes (eg, chronic pain prevalence), (3) broad findings, (4) types of mechanisms proposed, and (5) policy or practice recommendations. Findings reveal a sharp increase in the number of published review articles on pain-related disparities since approximately the year 2000. The most commonly studied social dimension was sex/gender, followed by race/ethnicity and age. Studies examining disparities by socioeconomic status, geography, or other categories were rarer. While most findings showed disadvantaged social groups to have worse pain outcomes, there were intriguing exceptions. Biological, psychological, and sociocultural mechanisms were considered much more frequently than sociostructural (macrolevel) ones. Policy/practice recommendations were typically individual-level behavioral suggestions for providers or patients. We identify high-priority areas for future research, including greater attention to lower-income countries, chronic pain prevention, and macrolevel drivers of pain disparities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".